This invention relates generally to segregating selected object images from an image. In particular this invention relates to segregating selected object images from images containing a collection of object images to remove background artifacts and image leaks that result from closely spaced object images-and to view selected objects image independently.
Image segregation herein means defining the boundaries of an object image from an image in order to separate the object image from noise and other object images. Segregating an object image from 2-dimensional images involves defining the boundaries of the image that correspond to the object. Defining the boundaries of an object image can be difficult, especially if the object image is blurred or is positioned in an area of the image where there is a collection of other object images that leak or bleed into each other. Separating an object image form a more complex image or defining the boundaries of an object image within an image is useful for the identification of the object that has been imaged. For example, the authenticity of a blurred photograph could be ascertained if the true shape of the i objects in the photograph can be discerned. Furthermore, image segregation can be used to support automated indexing and retrieval of photographs from large photograph collections based on the photographs"" contents, or could facilitate identification of bank robbers from poor quality video images. Of all the possible applications of image segregation, one of more useful applications has been in the area of medical imaging.
Computed Tomography (CT) is an important non-evasive method to image tissue volumes for medical diagnoses and therapy planning. The general concept of CT imaging involves collecting multiple stepwise images of a circumference of a tissue slice. A 2-dimensional image is reconstructed by back projection of the detected signals densities on to the imaged slice. Any number of 2-dimensional slices are acquired and can be stacked to generate a 3-dimensional image volume representing the imaged tissue.
Virtually all volume imaging methods use digitizing image data. Digitizing the image data allows a computer system to manipulate the data and stack the 2-dimensional images into a 3-dimensional image volume. Once the image volume is constructed the volume can be edited by any number of methods. There are several image acquisition methods, such as ultrasound and Magnetic Resonance (MR), for generating image volumes that use the same basic principle of generating 2-dimensional digitized images and stacking them to generate a 3-dimensional image volume which then can be manipulated either sectional or as an image volume by a computer.
It is often beneficial to selectively view a particular feature or object image in the imaged volume by a process of image segregation. For example, vascular tissue is difficult to view in the complex surrounding of an image containing large organs and bone tissue. There are many exemplar methods for image segregation; for a review see Pal, N. R., and Pal, S. K. A review on image segmentation techniques, Pattern Recognition, Volume 26(9), pp. 1277-1294. Image segregation in a three dimensional volume involves two basic steps. Firstly, at least one two dimensional cross section needs to be edited to select the image that is to be segmented. For example, a region of pixels containing Voxels of the object image of interest can be traced and the pixels outside of the traced region can have the image intensity set to zero. Prior art segregation methods involve manual tracing of the object image to be segmented. The procedure of manual tracing is very time consuming even for experienced radiologists and the results are not consistent from operator to operator. Image thresholding is another common technique for segmenting object images from an image. The basic principle is to assign image thresholds that correspond to the imaged object to be segmented. However, object images that appear in medical images often have overlapping intensity distribution and thresholding can result in the misclassification of Voxels. Users usually distinguish between good and inadequate thresholds by the shape of the regions whose Voxels intensities are contained within the thresholds and a priori knowledge of the shape of the object imaged. Deciding what is a good intensity threshold can be time consuming and can give unreasonable results because of overlapping intensities.
The second step to image segregation in three dimensions involves identifying and grouping the object images from each consecutive 2-dimensional cross section that belongs to the object to be segmented based on grouping criteria. For example the grouping criteria could be that object images that have a similar image intensity and position. Generally, all the method for image segregation in 2-dimensions can be extended to three dimensions but still have the same limitations and difficulties.
What is needed is a method for object image segregation that can be automated, does not require hand tracing and does not require a priori knowledge of shapes of the objects imaged. Further there is a need for a method that can segregate complex structures such as vascular tissue images. The method should reduce the time involve in the segregation, reduce artifacts in the image caused by leaks from surrounding object and be readily applied to image volumes.
It is a primary object of this invention to provide a method for segregating object images from images, whereby the segregation method requires no a priori knowledge about the shapes of the object imaged or the object images. The method is general and can be used for segregating object images from continues images such as photographs and film
It is a further object of this invention to provide a method for segregating object images from an image using information obtained from intensity thresholding and the inherent shapes of the object images. The method generates segregated images of object that accurately represent the features of the object imaged.
It is a specific object of this invention to provide a method of segregating object images from images, wherein the method does not require manual tracing. The method eliminates the inconstancies that occur from operator tracing, reduces the time of segregating the object images, is not specific or sensitive to the image acquisition mode and can be fully automated.
It is a more specific object of the invention to provide a method of segregating complex object images from images. The method has applications in medical imaging and is particularly useful for segregating vessel tissue from an image volume, wherein the image volume is constructed from digitized data acquisitions obtained ultrasound, CT, and MR imaging techniques.
The objects of the invention are obtained providing a method for segmenting complex object images, such as arterial structures, from an image volume. The image volume is preferably obtained by acquiring 2-dimensional images that represent imaged slices of an image volume. The method is most useful in the field of medical imaging where the 2-dimensional images are digitized images obtained from CT, MR or ultrasound acquisition data and are comprised of Voxels. While it is not required, in the preferred embodiment of this invention the 2-dimensional images are segmented to define the salient regions of the images containing the object images to be segmented prior to constructing a 3-dimensional image volume.
There are any number of methods to segment object images from 2-dimensional images using intensity thresholding, but the method of this invention uses the combinations of intensity thresholding and intrinsic imaged shapes in order to segment object images. The method can be completely automated and does not require prior knowledge of the shapes of the objects. imaged.
An intensity threshold or range of intensities is predetermined to give a sufficient number of divisions of intensities depending on the range of image intensities contained within the images. Isolabel contours are then generated around object images, whereby the spatial separations of the contours are determined by the intensity thresholds. The isolabel contours follow a path of substantially constant intensity and enclose Voxels within regions that result from the intensity thresholds.
The Voxels between contours are then assigned a constant intensity value or label. Image intensity can be represented by a numerical intensity, image density, image brightness and image color; in the case of digitized images, image densities or gray scales are the most common representation of intensity. To decide which contours belong to which object image the shapes of the isolabel contours are compared. It is generally preferable to compare the contours starting from the inner portion of an object image and moving outward, however it is not required. When the shape of a contour deviates substantially from the shape of other contours, the contour with the deviating shape can be considered as not belonging to the same object image as the other contours and the Voxels within the contour are labeled accordingly. For example, if a group of isolabel contours are generated around an approximately spherical object, starting from the inward region of the object outward, when isolabel contours deviate substantially from spherical they can be considered as not belonging to the same group of contours. The contours that deviate substantially from groups of contours around an object image to be segmented, can be labeled by setting the Voxels between the deviating contours to zero and the contours that have substantially similar shape are labeled as belonging to the same object image. In comparing the shapes of the contours, it is beneficial to compare shapes in the order of increased intensity values or labels and to maintain a hierarchical representation of the contours to improve the efficiency of the comparison. The method requires no prior knowledge of the shapes of the object images and uses the combination of intensity thresholding and shape comparison to identify the regions of image intensity that belong to an object image. The method is general and can be applied to continuos intensity images such as photographs and films.
Once the 2-dimensional images are obtained, the images are stored in a computer that is capable of constructing a 3-dimensional image volume. The volume that is generated does not connect or group object images from the 2-dimensional images sufficiently to segment a particular object image from the volume, especially when the object image is a complex object or is contained in a complex set of 2-dimensional images. To allow such object images to be segmented the imaged volume can be divided into a set of parallel adjacent 2-dimensional planes in any direction. Images of the objects contained within the image volume are then viewed as cross sections of the object image as it passes through the 2-dimensional planes. Isolabel contours are then generated from cross sections of the object images in each 2-dimensional plane or sets of planes taken from the 3-dimensional image volume.
A preferred method for generating isolabel contours is to choose a set of control points within each image intensity threshold of a 2-dimensional plane and connect the set of control points with a cubic spline approximation line. The process is iterated by choosing (n) sets of control points within the image intensity threshold to generated (n) pseudo isolabel contours. The (n) pseudo isolabel contours are then averaged to generate an isolabel contour that represents the shape of a cross section of an object image in a 2-dimensional plane. The isolabel contours generated by this method can be described by a mathematical function that is used to describe the shape of the cross sections. In order to describe the shapes of the isolabel contours, a turning angle sequence for each isolabel contour is calculated. The turning angle sequences of the isolabel contours are compared to determine which contours belong to the group of contours associated with the objects to be segmented from the image volume. Contours within a 2-dimensional plane that have turning angle sequences that deviate substantially from the group of contours associated with the object image to be segments are labeled as not belonging to that object image. Regions of the image contained between the contours that exhibit sufficient shape deviation may be edited out of the imaged volume by setting the image intensity to zero.
Once the contours have been generated by thresholding, the shapes of the contours have been compared, and Voxels have been labeled for a set of adjacent 2-dimensional planes of the imaged volume, the cross sections of object images are examined between adjacent planes in order to group inter-planar cross sections of object images. To decide if cross sections of object images between adjacent 2-dimensional planes belong to the same object image, an overlap criteria is applied in the direction normal to the 2-dimensional planes.
The model that is applied to analyze the overlap criteria is that object images are represented by compositions of cylinders within the 3-dimensional volume. If object image are appropriately modeled as cylinders within the image volume, then cross sections of object images in 2-dimensional planes can be modeled as ellipses.
Calculating the overlaps of cross sectional segments of object images from sets of 2-dimensional adjacent planes of the imaged volume is accomplished by prescribing a functions that substantially encloses the area of cross-sections. Preferably, this is accomplished by describing cross sections as enclosed by contours that have been generated by the method described above. The contour function for a given cross section is decomposed into two orthogonal projections in the plane of the cross section. By computing the first harmonic coefficients of the Fourier series for this function a new function that approximates the shapes of the cross sections as ellipse is generated based on the contour function. The ellipses in the adjacent parallel 2-dimensional planes are then examined for their overlap in the direction normal to the 2-dimensional planes.
As a first overlap criteria to decide which ellipses to evaluate the minor-axes of the ellipses are compared. If minor-axes of ellipses in adjacent parallel 2-dimensional planes are substantially the same length and have substantially the same orientation they potentially belong to the same object image and should be evaluated further. If this condition is not met the ellipses may be considered as not belonging to the same object image. A second overlap criteria involves evaluating the overlap integrals of the ellipses in the direction normal to 2-dimensional planes. If the overlap integral is not sufficient the cross sections are label as not belonging to same object image or can be edited out of the image volume.
To further check and see if the model is correct, the overlap integral values from the ellipses are compared to the overlap valued of the functions or contours from which the ellipse where derived. In order to segment complex object images from an imaged volume it is preferred to extent the method described above to three orthogonal directions.